{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "from tqdm import trange\n",
    "from tqdm import tqdm\n",
    "from skimage.util import montage\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import torch.utils.data as data\n",
    "import torchvision.transforms as transforms\n",
    "\n",
    "import medmnist\n",
    "from medmnist.dataset import PathMNIST, ChestMNIST, DermaMNIST, OCTMNIST, PneumoniaMNIST, RetinaMNIST, BreastMNIST, OrganMNISTAxial, OrganMNISTCoronal, OrganMNISTSagittal\n",
    "from medmnist.evaluator import getAUC, getACC\n",
    "from medmnist.info import INFO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Version: 0.2.0\n"
     ]
    }
   ],
   "source": [
    "print(\"Version:\", medmnist.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_flag = 'pneumoniamnist'\n",
    "# data_flag = 'breastmnist'\n",
    "download = True\n",
    "input_root = 'tmp_data/'\n",
    "\n",
    "NUM_EPOCHS = 10\n",
    "BATCH_SIZE = 128\n",
    "lr = 0.001\n",
    "\n",
    "flag_to_class = {\n",
    "    \"pathmnist\": PathMNIST,\n",
    "    \"chestmnist\": ChestMNIST,\n",
    "    \"dermamnist\": DermaMNIST,\n",
    "    \"octmnist\": OCTMNIST,\n",
    "    \"pneumoniamnist\": PneumoniaMNIST,\n",
    "    \"retinamnist\": RetinaMNIST,\n",
    "    \"breastmnist\": BreastMNIST,\n",
    "    \"organmnist_axial\": OrganMNISTAxial,\n",
    "    \"organmnist_coronal\": OrganMNISTCoronal,\n",
    "    \"organmnist_sagittal\": OrganMNISTSagittal,\n",
    "}\n",
    "\n",
    "DataClass = flag_to_class[data_flag]\n",
    "\n",
    "info = INFO[data_flag]\n",
    "task = info['task']\n",
    "n_channels = info['n_channels']\n",
    "n_classes = len(info['label'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we read the MedMNIST data, preprocess them and encapsulate them into dataloader form."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using downloaded and verified file: tmp_data/pneumoniamnist.npz\n",
      "Using downloaded and verified file: tmp_data/pneumoniamnist.npz\n",
      "Using downloaded and verified file: tmp_data/pneumoniamnist.npz\n"
     ]
    }
   ],
   "source": [
    "# preprocessing\n",
    "data_transform = transforms.Compose([\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize(mean=[.5], std=[.5])\n",
    "])\n",
    "\n",
    "# load the data\n",
    "train_dataset = DataClass(root=input_root, split='train', transform=data_transform, download=download)\n",
    "test_dataset = DataClass(root=input_root, split='test', transform=data_transform, download=download)\n",
    "nonorm_dataset = DataClass(root=input_root, split='train', transform=transforms.ToTensor(), download=download)\n",
    "\n",
    "# encapsulate data into dataloader form\n",
    "train_loader = data.DataLoader(dataset=train_dataset, batch_size=BATCH_SIZE, shuffle=True)\n",
    "test_loader = data.DataLoader(dataset=test_dataset, batch_size=BATCH_SIZE, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset PneumoniaMNIST\n",
      "    Number of datapoints: 4708\n",
      "    Root location: tmp_data/\n",
      "    Split: train\n",
      "    Task: binary-class\n",
      "    Number of channels: 1\n",
      "    Meaning of labels: {'0': 'normal', '1': 'pneumonia'}\n",
      "    Number of samples: {'train': 4708, 'val': 524, 'test': 624}\n",
      "    Description: PneumoniaMNIST: A dataset based on a prior dataset of 5,856 pediatric chest X-ray images. The task is binary-class classification of pneumonia and normal. We split the source training set with a ratio of 9:1 into training and validation set, and use its source validation set as the test set. The source images are single-channel, and their sizes range from (384-2,916) x (127-2,713). We center-crop the images and resize them into 1 x 28 x 28.\n",
      "    License: CC BY 4.0\n",
      "===================\n",
      "Dataset PneumoniaMNIST\n",
      "    Number of datapoints: 624\n",
      "    Root location: tmp_data/\n",
      "    Split: test\n",
      "    Task: binary-class\n",
      "    Number of channels: 1\n",
      "    Meaning of labels: {'0': 'normal', '1': 'pneumonia'}\n",
      "    Number of samples: {'train': 4708, 'val': 524, 'test': 624}\n",
      "    Description: PneumoniaMNIST: A dataset based on a prior dataset of 5,856 pediatric chest X-ray images. The task is binary-class classification of pneumonia and normal. We split the source training set with a ratio of 9:1 into training and validation set, and use its source validation set as the test set. The source images are single-channel, and their sizes range from (384-2,916) x (127-2,713). We center-crop the images and resize them into 1 x 28 x 28.\n",
      "    License: CC BY 4.0\n",
      "===================\n",
      "Dataset PneumoniaMNIST\n",
      "    Number of datapoints: 4708\n",
      "    Root location: tmp_data/\n",
      "    Split: train\n",
      "    Task: binary-class\n",
      "    Number of channels: 1\n",
      "    Meaning of labels: {'0': 'normal', '1': 'pneumonia'}\n",
      "    Number of samples: {'train': 4708, 'val': 524, 'test': 624}\n",
      "    Description: PneumoniaMNIST: A dataset based on a prior dataset of 5,856 pediatric chest X-ray images. The task is binary-class classification of pneumonia and normal. We split the source training set with a ratio of 9:1 into training and validation set, and use its source validation set as the test set. The source images are single-channel, and their sizes range from (384-2,916) x (127-2,713). We center-crop the images and resize them into 1 x 28 x 28.\n",
      "    License: CC BY 4.0\n"
     ]
    }
   ],
   "source": [
    "print(train_dataset)\n",
    "print(\"===================\")\n",
    "print(test_dataset)\n",
    "print(\"===================\")\n",
    "print(nonorm_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualization\n",
    "\n",
    "img, target = nonorm_dataset[0]\n",
    "if n_channels == 1:\n",
    "    img = img.reshape(28, 28)\n",
    "    plt.imshow(img, cmap='gray')\n",
    "else:\n",
    "    img = img.permute(1, 2, 0)\n",
    "    plt.imshow(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# montage\n",
    "\n",
    "def process(n_channels, length=20):\n",
    "    scale = length * length\n",
    "\n",
    "    image = np.zeros((scale, 28, 28, 3)) if n_channels == 3 else np.zeros((scale, 28, 28))\n",
    "    index = [i for i in range(scale)]\n",
    "    np.random.shuffle(index)\n",
    "    \n",
    "    for idx in range(scale):\n",
    "        img, _ = nonorm_dataset[idx]\n",
    "        if n_channels == 3:\n",
    "            img = img.permute(1, 2, 0).numpy()\n",
    "        else:\n",
    "            img = img.reshape(28, 28).numpy()\n",
    "        image[index[idx]] = img\n",
    "\n",
    "    if n_channels == 1:\n",
    "        image = image.reshape(scale, 28, 28)\n",
    "        arr_out = montage(image)\n",
    "        plt.imshow(arr_out, cmap='gray')\n",
    "    else:\n",
    "        image = image.reshape(scale, 28, 28, 3)\n",
    "        arr_out = montage(image, multichannel=3)\n",
    "        plt.imshow(arr_out)\n",
    "    \n",
    "process(n_channels=n_channels, length=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, we define a simple model for illustration, object function and optimizer that we use to classify."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define a simple CNN model\n",
    "\n",
    "class Net(nn.Module):\n",
    "    def __init__(self, in_channels, num_classes):\n",
    "        super(Net, self).__init__()\n",
    "\n",
    "        self.layer1 = nn.Sequential(\n",
    "            nn.Conv2d(in_channels, 16, kernel_size=3),\n",
    "            nn.BatchNorm2d(16),\n",
    "            nn.ReLU())\n",
    "\n",
    "        self.layer2 = nn.Sequential(\n",
    "            nn.Conv2d(16, 16, kernel_size=3),\n",
    "            nn.BatchNorm2d(16),\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool2d(kernel_size=2, stride=2))\n",
    "\n",
    "        self.layer3 = nn.Sequential(\n",
    "            nn.Conv2d(16, 64, kernel_size=3),\n",
    "            nn.BatchNorm2d(64),\n",
    "            nn.ReLU())\n",
    "        \n",
    "        self.layer4 = nn.Sequential(\n",
    "            nn.Conv2d(64, 64, kernel_size=3),\n",
    "            nn.BatchNorm2d(64),\n",
    "            nn.ReLU())\n",
    "\n",
    "        self.layer5 = nn.Sequential(\n",
    "            nn.Conv2d(64, 64, kernel_size=3, padding=1),\n",
    "            nn.BatchNorm2d(64),\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool2d(kernel_size=2, stride=2))\n",
    "\n",
    "        self.fc = nn.Sequential(\n",
    "            nn.Linear(64 * 4 * 4, 128),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(128, 128),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(128, num_classes))\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.layer1(x)\n",
    "        x = self.layer2(x)\n",
    "        x = self.layer3(x)\n",
    "        x = self.layer4(x)\n",
    "        x = self.layer5(x)\n",
    "        x = x.view(x.size(0), -1)\n",
    "        x = self.fc(x)\n",
    "        return x\n",
    "\n",
    "model = Net(in_channels=n_channels, num_classes=n_classes)\n",
    "    \n",
    "# define loss function and optimizer\n",
    "if task == \"multi-label, binary-class\":\n",
    "    criterion = nn.BCEWithLogitsLoss()\n",
    "else:\n",
    "    criterion = nn.CrossEntropyLoss()\n",
    "    \n",
    "optimizer = optim.SGD(model.parameters(), lr=lr, momentum=0.9)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we can start to train and evaluate!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 37/37 [00:11<00:00,  3.10it/s]\n",
      "100%|██████████| 37/37 [00:09<00:00,  3.84it/s]\n",
      "100%|██████████| 37/37 [00:09<00:00,  3.82it/s]\n",
      "100%|██████████| 37/37 [00:09<00:00,  3.76it/s]\n",
      "100%|██████████| 37/37 [00:08<00:00,  4.17it/s]\n",
      "100%|██████████| 37/37 [00:09<00:00,  4.01it/s]\n",
      " 51%|█████▏    | 19/37 [00:05<00:04,  3.72it/s]"
     ]
    }
   ],
   "source": [
    "# train\n",
    "\n",
    "for epoch in range(NUM_EPOCHS):\n",
    "    train_correct = 0\n",
    "    train_total = 0\n",
    "    test_correct = 0\n",
    "    test_total = 0\n",
    "    \n",
    "    model.train()\n",
    "    for inputs, targets in tqdm(train_loader):\n",
    "        # forward + backward + optimize\n",
    "        optimizer.zero_grad()\n",
    "        outputs = model(inputs)\n",
    "        \n",
    "        if task == 'multi-label, binary-class':\n",
    "            targets = targets.to(torch.float32)\n",
    "            loss = criterion(outputs, targets)\n",
    "        else:\n",
    "            targets = targets.squeeze().long()\n",
    "            loss = criterion(outputs, targets)\n",
    "        \n",
    "        loss.backward()\n",
    "        optimizer.step()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# evaluation\n",
    "\n",
    "def test(split):\n",
    "    model.eval()\n",
    "    y_true = torch.tensor([])\n",
    "    y_score = torch.tensor([])\n",
    "    \n",
    "    data_loader = train_loader if split == 'train' else test_loader\n",
    "\n",
    "    with torch.no_grad():\n",
    "        for inputs, targets in data_loader:\n",
    "            outputs = model(inputs)\n",
    "\n",
    "            if task == 'multi-label, binary-class':\n",
    "                targets = targets.to(torch.float32)\n",
    "                m = nn.Sigmoid()\n",
    "                outputs = m(outputs)\n",
    "            else:\n",
    "                targets = targets.squeeze().long()\n",
    "                m = nn.Softmax(dim=1)\n",
    "                outputs = m(outputs)\n",
    "                targets = targets.float().resize_(len(targets), 1)\n",
    "\n",
    "            y_true = torch.cat((y_true, targets), 0)\n",
    "            y_score = torch.cat((y_score, outputs), 0)\n",
    "\n",
    "        y_true = y_true.numpy()\n",
    "        y_score = y_score.detach().numpy()\n",
    "        auc = getAUC(y_true, y_score, task)\n",
    "        acc = getACC(y_true, y_score, task)\n",
    "    \n",
    "        print('%s  acc: %.3f  auc:%.3f' % (split, acc, auc))\n",
    "\n",
    "        \n",
    "print('==> Evaluating ...')\n",
    "test('train')\n",
    "test('test')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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